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Molecular Dynamics Methods Development

I have developed 4 different methods for enhanced sampling of molecular dynamics simulations (3 published in Journal of Chemical Physics, 1 published in Journal of Chemical Information and Modeling). My future plans include both further work in this vein of theory and methods development, as well as implementation of my methods into more generalized (and publicly available) software. For a few examples of this type of work, please see:

Advances in milestoning. I. Enhanced sampling via wind-assisted reweighted milestoning (WARM):

https://aip.scitation.org/doi/abs/10.1063/1.5029954

Advances in milestoning. II. Calculating time-correlation functions from milestoning using stochastic path integrals:

https://aip.scitation.org/doi/abs/10.1063/1.5037482

Automated placement of interfaces in conformational kinetics calculations using machine learning:

https://aip.scitation.org/doi/abs/10.1063/1.4989857

Machine Learning Applications for the Molecular Sciences

Another main focus of my research is the development of machine learning-based methods for both setting up and interpreting the results of molecular simulations. I have developed machine learning-based methodology for studying the kinetics of both configurational changes in complex macromolecules, as well as multiple pathway chemical kinetics. I am also interested in developing machine learning-based methodology for interpreting experimental data. For examples of this type of work, please see the following:

Predicting Reaction Products and Automating Reactive Trajectory Characterization in Molecular Simulations with Support Vector Machines:

https://pubs.acs.org/doi/abs/10.1021/acs.jcim.9b00134

Comparative Exploratory Analysis of Intrinsically Disordered Protein Dynamics Using Machine Learning and Network Analytic Methods

https://doi.org/10.3389/fmolb.2019.00042

Automated placement of interfaces in conformational kinetics calculations using machine learning:

https://aip.scitation.org/doi/abs/10.1063/1.4989857

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Coarse-grained Modeling of Biophysical Systems

My third research focus is the development of coarse-grained modeling techniques for molecular systems whose complexity make atomistic models intractable on the time scales of interest. I have built models for phenomena such as force-modulated catalytic activity of enzymes and protein aggregation events, such as the self-assembly of amyloid fibrils. For examples of this type of work, please see the following:

Network-Based Classification and Modeling of Amyloid Fibrils:

https://pubs.acs.org/doi/abs/10.1021/acs.jpcb.9b03494

A Fokker-Planck type model of force-modulated catalytic activity in thioredoxyn:

https://aip.scitation.org/doi/abs/10.1063/1.4926664

Local Graph Stability in Exponential Family Random Graph Models

https://arxiv.org/abs/1908.09470

Google Scholar Profile

If you are interested in reading more about my research, please click on the link to the right to see my Google Scholar profile.